Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv”
Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv””
Merge datasets

## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data

## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data

### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))

### FINISH THE CODE HERE ###
# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

### FINISH THE CODE HERE
cv_states <- merge(cv_states, state_pops, by="state")

2. Look at the data

Inspect the dimensions, head, and tail of the data
Inspect the structure of each variables. Are they in the correct format?

dim(cv_states)

## [1] 51074     9

head(cv_states)

##     state       date fips   cases deaths geo_id population pop_density abb
## 1 Alabama 2021-07-15    1  557578  11439      1    4887871    96.50939  AL
## 2 Alabama 2021-07-28    1  577463  11510      1    4887871    96.50939  AL
## 3 Alabama 2021-08-27    1  681828  12153      1    4887871    96.50939  AL
## 4 Alabama 2021-06-08    1  546540  11220      1    4887871    96.50939  AL
## 5 Alabama 2022-03-17    1 1290692  18998      1    4887871    96.50939  AL
## 6 Alabama 2021-07-30    1  580193  11516      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips  cases deaths geo_id population pop_density abb
## 51069 Wyoming 2021-11-08   56 105990   1243     56     577737    5.950611  WY
## 51070 Wyoming 2022-11-01   56 178866   1914     56     577737    5.950611  WY
## 51071 Wyoming 2021-08-15   56  68272    793     56     577737    5.950611  WY
## 51072 Wyoming 2022-11-08   56 179366   1917     56     577737    5.950611  WY
## 51073 Wyoming 2022-02-16   56 153663   1689     56     577737    5.950611  WY
## 51074 Wyoming 2022-01-20   56 133495   1601     56     577737    5.950611  WY

str(cv_states)

## 'data.frame':    51074 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : IDate, format: "2021-07-15" "2021-07-28" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  557578 577463 681828 546540 1290692 580193 551298 1531305 546845 1479605 ...
##  $ deaths     : int  11439 11510 12153 11220 18998 11516 11358 20533 11249 20048 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

3. Format the data

Make date into a date variable
Make state into a factor variable
Order the data first by state, second by date
Confirm the variables are now correctly formatted
Inspect the range values for each variable. What is the date range? The range of cases and deaths?

# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

### FINISH THE CODE HERE 
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]

# Confirm the variables are now correctly formatted
str(cv_states)

## 'data.frame':    51074 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...

head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 871 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 676 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 928 Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 417 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 714 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 167 Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips  cases deaths geo_id population pop_density abb
## 50232 Wyoming 2022-11-03   56 178866   1914     56     577737    5.950611  WY
## 50842 Wyoming 2022-11-04   56 178866   1914     56     577737    5.950611  WY
## 50409 Wyoming 2022-11-05   56 178866   1914     56     577737    5.950611  WY
## 50830 Wyoming 2022-11-06   56 178866   1914     56     577737    5.950611  WY
## 50524 Wyoming 2022-11-07   56 178866   1914     56     577737    5.950611  WY
## 51072 Wyoming 2022-11-08   56 179366   1917     56     577737    5.950611  WY

# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 871 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 676 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 928 Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 417 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 714 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 167 Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

summary(cv_states)

##            state            date                 fips           cases         
##  Washington   : 1023   Min.   :2020-01-21   Min.   : 1.00   Min.   :       1  
##  Illinois     : 1020   1st Qu.:2020-11-02   1st Qu.:16.00   1st Qu.:   88644  
##  California   : 1019   Median :2021-07-05   Median :29.00   Median :  341318  
##  Arizona      : 1018   Mean   :2021-07-05   Mean   :29.78   Mean   :  811407  
##  Massachusetts: 1012   3rd Qu.:2022-03-08   3rd Qu.:44.00   3rd Qu.:  954938  
##  Wisconsin    : 1008   Max.   :2022-11-08   Max.   :72.00   Max.   :11390274  
##  (Other)      :44974                                                          
##      deaths          geo_id        population        pop_density       
##  Min.   :    0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
##  1st Qu.: 1364   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
##  Median : 5104   Median :29.00   Median : 4468402   Median :  107.860  
##  Mean   :11352   Mean   :29.78   Mean   : 6404128   Mean   :  422.942  
##  3rd Qu.:14375   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
##  Max.   :97064   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
##                                                     NA's   :971        
##       abb       
##  WA     : 1023  
##  IL     : 1020  
##  CA     : 1019  
##  AZ     : 1018  
##  MA     : 1012  
##  WI     : 1008  
##  (Other):44974

min(cv_states$date)

## [1] "2020-01-21"

max(cv_states$date)

## [1] "2022-11-08"

4. Add `new_cases` and `new_deaths` and correct outliers

Add variables for new cases, new_cases, and new deaths, new_deaths:
- Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2
Filter to dates after June 1, 2022
Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
Correct outliers: Set negative values for new_cases or new_deaths to 0
Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths
Get the rolling average of new cases and new deaths to smooth over time
Inspect data again interactively

# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]
  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]
  #### FINISH THE CODE HERE ###
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] 
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
cv_states <- cv_states %>% dplyr::filter(date >= "2022-06-01")

### FINISH THE CODE HERE ###
p1<-ggplot(cv_states, 
           aes( x=date, y=new_cases, color=state  )
           ) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)

p1<-NULL # to clear from workspace
### FINISH THE CODE HERE ###
p2<-ggplot(cv_states, 
           aes(x=date, y=new_deaths, color=state )
           ) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updates `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)

# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

#p2=NULL

# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

#p2=NULL

5. Add additional variables

Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:
- per100k = cases per 100,000 population
- newper100k= new cases per 100,000
- deathsper100k = deaths per 100,000
- newdeathsper100k = new deaths per 100,000
Add a “naive CFR” variable representing deaths / cases on each date for each state
Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))

# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))

# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))

Lab 11 - Interactive Visualization

11/09/2022

Learning Goals

Lab Description

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

2. Look at the data

3. Format the data

4. Add `new_cases` and `new_deaths` and correct outliers

5. Add additional variables

Lab 11 - Interactive Visualization

11/09/2022

Learning Goals

Lab Description

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

2. Look at the data

3. Format the data

4. Add new_cases and new_deaths and correct outliers

5. Add additional variables

4. Add `new_cases` and `new_deaths` and correct outliers